Assessing Credit Risk in High-Yield Bonds: A guide to factors to consider

This article is my second about the yield market, following my recent experiences and insights from the 34th Annual High Yield Conference.

It delves into several key trends discussed in the conference, such as the implications of looser documentation, increased basket capacity, and flexible terms for issuers and investors. The necessity of repricing in response to market changes was discussed as well as the role of high-yield bond indices in investment strategies, the significance of distressed levels in evaluating risk, the impact of softer Federal Reserve tightening on borrowing costs, the growing importance of ESG considerations in investment decisions, and the innovative application of AI in credit risk measurement.

I am very excited about these recent developments since they support my view about where the opportunities are in the next 2-5 years.

Before the main points, as a refresher or for those not familiar with the general classification of hedge funds, here is a table:

This article delves into several key trends discussed in the conference:

1. Looser Documentation, Increased Basket Capacity, and Flexible Terms: Examines the trend towards more borrower-friendly covenants and its effects on the market.
2. Repricing and Its Necessity: Discusses the reasons and implications of repricing high-yield bonds in response to market shifts.
3. High Yield Bond Index and Purchasing: Explains the function of high-yield bond indices and how investors can access these markets through index funds or ETFs.
4. Distressed Levels and hedge fund interest: Explores the risks and opportunities associated with investing in distressed securities, as well as prominent hedge funds affecting the space.
5. Softer Fed Tightening: Considers the impact of a more gradual approach to interest rate increases by the Federal Reserve on the high-yield market.
6. ESG Considerations: Highlights the growing importance of environmental, social, and governance factors in assessing the sustainability and ethics of high-yield investments.
7. Credit Risk forecast via AI: Looks at the transformative role of artificial intelligence in enhancing credit risk analysis and portfolio management

Looser Documentation, Increased Basket Capacity, and Flexible Terms

Looser documentation in the context of high-yield bonds refers to the trend of bond covenants becoming less stringent, which can lead to more borrower-friendly terms. This often includes a broader set of 'permitted liens' and 'permitted investments,' allowing issuers more flexibility in how they manage their assets and liabilities.

Increased basket capacity relates to the negotiated carve-outs within the bond covenants that permit the issuer to incur additional debt or make investments that would otherwise be restricted. These baskets are tailored to the issuer's needs and strategic business plan, providing them with more room to maneuver financially without breaching covenant terms

Flexible terms may also include features like portability, which allows a bond to be transferred in a change of control situation without triggering a put option, or looser maintenance covenants, which are financial ratios the issuer must maintain to avoid default.

Repricing and Its Necessity

Repricing in the high-yield market occurs when there is a reassessment of the value of an investment due to changes in the market environment, such as interest rate fluctuations or changes in the issuer's creditworthiness. It is necessary when the market conditions have shifted in such a way that the current interest rates on existing debt are no longer aligned with the market. For example, if average clearing spreads for new issues decrease, issuers may seek to reprice their existing debt to lower their borrowing costs, taking advantage of the narrower spread between the new issue rates and the rates on their outstanding debt.

High Yield Bond Index and Purchasing

A high-yield bond index measures the performance of a basket of high-yielding corporate bonds, reflecting the market-value-weighted performance of these securities. Indices like the S&P U.S. High Yield Corporate Bond Index are rebalanced monthly and provide a benchmark for the high-yield bond market.

Investors cannot directly buy an index, but they can invest in index funds or exchange-traded funds (ETFs) that aim to replicate the performance of a high-yield bond index. These funds can be purchased through brokerage accounts, and the minimum investment will depend on the specific fund's requirements.

Distressed Levels and hedge fund interest

Distressed levels refer to the trading status of securities issued by a company that is near or going through bankruptcy, or that has breached certain covenants. These securities are considered distressed because the issuer is struggling to meet its financial obligations, leading to a substantial reduction in the value of its financial instruments. Distressed securities are high-risk but can offer the potential for high returns to investors willing to accept that risk.

Investing in distressed debt is a specialized area that requires a deep understanding of the risks and potential rewards associated with companies facing financial difficulties Thus some large players might have a tremendous impact in the risk/return profile of selected opportunites. Some prominent hedge funds that are known for their activities in this area include:

• Oaktree Capital Management: Founded by Howard Marks and Bruce Karsh, Oaktree is renowned for its expertise in distressed securities. The firm is known for its contrarian approach and has a long history of investing in distressed debt markets.?
• Elliott Management Corporation: Founded by Paul Singer, Elliott Management is a multi-strategy fund known for its activist approach, including in distressed situations. The fund often takes significant positions in troubled companies and pushes for changes to increase value (as a side note, it was this hedge fund that acquired the intellectual property assets of my computational linguistics startup ttwick, which specialized in deep neural networks for knowledge discovery. Elliot later sold ttwick’s patents to a prominent Silicon Valley tech firm (can you guess?), in a typical “distressed asset” trade).?
• Aurelius Capital Management: This fund is known for its focus on distressed investing and has been involved in several high-profile distressed situations, often taking an active role in the restructuring process of the acquired company.
• Mudrick Capital Management: Specializing in distressed credit, Mudrick Capital employs a value-oriented approach to investing, often engaging deeply in the operational aspects of the companies in which it invest.
• Solus Alternative Asset Management: Solus is another hedge fund that specializes in distressed and deep-value investing, seeking opportunities in companies that are undergoing or are likely to undergo significant change.

Softer Fed Tightening

Softer Fed tightening implies a more gradual or less aggressive approach by the Federal Reserve to increase interest rates. This can occur when the Fed aims to slow down inflation without significantly hampering economic growth. Softer tightening may lead to a more stable or favorable borrowing environment, as drastic increases in interest rates can lead to higher borrowing costs and potentially slow down economic activity.

ESG Considerations

Environmental, Social, and Governance (ESG) considerations refer to the three central factors in measuring the sustainability and ethical impact of an investment in a company. ESG factors include how a company performs as a steward of the natural environment, how it manages relationships with employees, suppliers, customers, and the communities where it operates, and a company's leadership, executive pay, audits, internal controls, and shareholder rights. High ESG standards are increasingly seen as indicators of a company's long-term viability and creditworthiness, as they can impact a company's reputation, legal risks, and operational performance.

AI in Credit Risk Measurement

AI and machine learning are increasingly being used in credit risk measurement to enhance the accuracy and efficiency of credit analysis. These tools can process vast amounts of data, including non-traditional data sources, to identify patterns that may not be evident to human analysts and predict rating transitions.

AI driven models could predict outcomes, assess credit risk, and monitor sentiment shifts in real-time, enabling proactive portfolio management and quicker decision-making. Financial institutions, credit rating agencies, and fintech companies are among those leveraging AI for credit risk analysis, using these technologies to automate data collection, perform deep data analysis, and generate predictive insights into borrower behavior and market trends.